How to use the playground

Playground introduction

The playground is a complete Gravitino Docker runtime environment with Hive, HDFS, Trino, MySQL, PostgreSQL, Jupyter, and a Gravitino server.

Depending on your network and computer, startup time may take 3-5 minutes. Once the playground environment has started, you can open http://localhost:8090 in a browser to access the Gravitino Web UI.

Prerequisites

Install Git and Docker Compose.

TCP ports used

The playground runs a number of services. The TCP ports used may clash with existing services you run, such as MySQL or Postgres.

Docker container	Ports used
playground-gravitino	8090 9001
playground-hive	3307 9000 9083
playground-mysql	3306
playground-postgresql	5342
playground-trino	8080
playground-jupyter	8888

Start playground

Launch all components of playground

git clone git@github.com:datastrato/gravitino-playground.git
cd gravitino-playground
./launch-playground.sh

Launch big data components of playground

git clone git@github.com:datastrato/gravitino-playground.git
cd gravitino-playground
./launch-playground.sh bigdata
# equivalent to
./launch-playground.sh hive gravitino trino postgresql mysql spark

Launch AI components of playground

git clone git@github.com:datastrato/gravitino-playground.git
cd gravitino-playground
./launch-playground.sh ai
# equivalent to
./launch-playground.sh hive gravitino mysql jupyter

Launch special component or components of playground

git clone git@github.com:datastrato/gravitino-playground.git
cd gravitino-playground
./launch-playground.sh hive|gravitino|trino|postgresql|mysql|spark|jupyter

Experiencing Gravitino Fileset with Jupyter

We provide a Fileset playground environment to help you quickly understand how to use Gravitino Python client to manage non-tabular data on HDFS via fileset in Gravitino service. You can refer document of Launch AI components of playground to launch a Gravitino server, HDFS and Jupyter notebook environment in you local Docker environment.

Waiting for the playground Docker environment to start, you can directly open http://localhost:8888/lab/tree/gravitino-fileset-sample.ipynb in the browser and run the example.

The gravitino-fileset-example contains the following code snippets:

1. Install HDFS Python client.
2. Create a HDFS client to connect HDFS and to do some test operations.
3. Install Gravitino Python client.
4. Initialize Gravitino admin client and create a Gravitino metalake.
5. Initialize Gravitino client and list metalakes.
6. Create a Gravitino Catalog and special type is Catalog.Type.FILESET and provider is hadoop
7. Create a Gravitino Schema with the location pointed to a HDFS path, and use hdfs client to check if the schema location is successfully created in HDFS.
8. Create a Fileset with type is Fileset.Type.MANAGED, use hdfs client to check if the fileset location was successfully created in HDFS.
9. Drop this Fileset.Type.MANAGED type fileset and check if the fileset location was successfully deleted in HDFS.
10. Create a Fileset with type is Fileset.Type.EXTERNAL and location pointed to exist HDFS path
11. Drop this Fileset.Type.EXTERNAL type fileset and check if the fileset location was not deleted in HDFS.

Experiencing Gravitino with Trino SQL

1. Log in to the Gravitino playground Trino Docker container using the following command:

docker exec -it playground-trino bash

2. Open the Trino CLI in the container.

trino@container_id:/$ trino

Example

Simple queries

You can use simple queries to test in the Trino CLI.

SHOW CATALOGS;

CREATE SCHEMA catalog_hive.company
  WITH (location = 'hdfs://hive:9000/user/hive/warehouse/company.db');

SHOW CREATE SCHEMA catalog_hive.company;

CREATE TABLE catalog_hive.company.employees
(
  name varchar,
  salary decimal(10,2)
)
WITH (
  format = 'TEXTFILE'
);

INSERT INTO catalog_hive.company.employees (name, salary) VALUES ('Sam Evans', 55000);

SELECT * FROM catalog_hive.company.employees;

SHOW SCHEMAS from catalog_hive;

DESCRIBE catalog_hive.company.employees;

SHOW TABLES from catalog_hive.company;

Cross-catalog queries

In a company, there may be different departments using different data stacks. In this example, the HR department uses Apache Hive to store its data and the sales department uses PostgreSQL. You can run some interesting queries by joining the two departments' data together with Gravitino.

To know which employee has the largest sales amount, run this SQL:

SELECT given_name, family_name, job_title, sum(total_amount) AS total_sales
FROM catalog_hive.sales.sales as s,
  catalog_postgres.hr.employees AS e
where s.employee_id = e.employee_id
GROUP BY given_name, family_name, job_title
ORDER BY total_sales DESC
LIMIT 1;

To know the top customers who bought the most by state, run this SQL:

SELECT customer_name, location, SUM(total_amount) AS total_spent
FROM catalog_hive.sales.sales AS s,
  catalog_hive.sales.stores AS l,
  catalog_hive.sales.customers AS c
WHERE s.store_id = l.store_id AND s.customer_id = c.customer_id
GROUP BY location, customer_name
ORDER BY location, SUM(total_amount) DESC;

To know the employee's average performance rating and total sales, run this SQL:

SELECT e.employee_id, given_name, family_name, AVG(rating) AS average_rating, SUM(total_amount) AS total_sales
FROM catalog_postgres.hr.employees AS e,
  catalog_postgres.hr.employee_performance AS p,
  catalog_hive.sales.sales AS s
WHERE e.employee_id = p.employee_id AND p.employee_id = s.employee_id
GROUP BY e.employee_id,  given_name, family_name;

Using Iceberg REST service

If you want to migrate your business from Hive to Iceberg. Some tables will use Hive, and the other tables will use Iceberg. Gravitino provides an Iceberg REST catalog service, too. You can use Spark to access REST catalog to write the table data. Then, you can use Trino to read the data from the Hive table joining the Iceberg table.

spark-defaults.conf is as follows (It's already configured in the playground):

spark.sql.extensions org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions
spark.sql.catalog.catalog_iceberg org.apache.iceberg.spark.SparkCatalog
spark.sql.catalog.catalog_iceberg.type rest
spark.sql.catalog.catalog_iceberg.uri http://gravitino:9001/iceberg/
spark.locality.wait.node 0

1. Login Spark container and execute the steps.

docker exec -it playground-spark bash

spark@container_id:/$ cd /opt/spark && /bin/bash bin/spark-sql

use catalog_iceberg;
create database sales;
use sales;
create table customers (customer_id int, customer_name varchar(100), customer_email varchar(100));
describe extended customers;
insert into customers (customer_id, customer_name, customer_email) values (11,'Rory Brown','rory@123.com');
insert into customers (customer_id, customer_name, customer_email) values (12,'Jerry Washington','jerry@dt.com');

2. Login Trino container and execute the steps. You can get all the customers from both the Hive and Iceberg table.

docker exec -it playground-trino bash

trino@container_id:/$ trino

select * from catalog_hive.sales.customers
union
select * from catalog_iceberg.sales.customers;